Biblio

In today’s world, Supervisory Control and Data Acquisition (SCADA) networks have many critical tasks, including managing infrastructure such as power, water, and sewage systems, and controlling automated manufacturing and transportation systems. Securing these systems is crucial. Here we describe a project to design security into an example system using formal specifications. Our example system is a component in a cybersecurity testbed at the University of Cincinnati, which was described in previous work. We also show how a design flaw can be discovered and corrected early in the system development process.

This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.